Università degli Studi eCampus

About: Università degli Studi eCampus is a education organization based out in Novedrate, Italy. It is known for research contribution in the topics: Anxiety & Planck. The organization has 124 authors who have published 538 publications receiving 21483 citations. The organization is also known as: Universita degli Studi eCampus.

Alzheimer’s disease cerebrospinal fluid biomarker in cognitively normal subjects

Abstract: In a large multicentre sample of cognitively normal subjects, as a function of age, gender and APOE genotype, we studied the frequency of abnormal cerebrospinal fluid levels of Alzheimer's disease biomarkers including: total tau, phosphorylated tau and amyloid-β1-42. Fifteen cohorts from 12 different centres with either enzyme-linked immunosorbent assays or Luminex® measurements were selected for this study. Each centre sent nine new cerebrospinal fluid aliquots that were used to measure total tau, phosphorylated tau and amyloid-β1-42 in the Gothenburg laboratory. Seven centres showed a high correlation with the new Gothenburg measurements; therefore, 10 cohorts from these centres are included in the analyses here (1233 healthy control subjects, 40-84 years old). Amyloid-β amyloid status (negative or positive) and neurodegeneration status (negative or positive) was established based on the pathological cerebrospinal fluid Alzheimer's disease cut-off values for cerebrospinal fluid amyloid-β1-42 and total tau, respectively. While gender did not affect these biomarker values, APOE genotype modified the age-associated changes in cerebrospinal fluid biomarkers such that APOE e4 carriers showed stronger age-related changes in cerebrospinal fluid phosphorylated tau, total tau and amyloid-β1-42 values and APOE e2 carriers showed the opposite effect. At 40 years of age, 76% of the subjects were classified as amyloid negative, neurodegeneration negative and their frequency decreased to 32% at 85 years. The amyloid-positive neurodegeneration-negative group remained stable. The amyloid-negative neurodegeneration-positive group frequency increased slowly from 1% at 44 years to 16% at 85 years, but its frequency was not affected by APOE genotype. The amyloid-positive neurodegeneration-positive frequency increased from 1% at 53 years to 28% at 85 years. Abnormally low cerebrospinal fluid amyloid-β1-42 levels were already frequent in midlife and APOE genotype strongly affects the levels of cerebrospinal fluid amyloid-β1-42, phosphorylated tau and total tau across the lifespan without influencing the frequency of subjects with suspected non-amyloid pathology.

Monitoring the public opinion about the vaccination topic from tweets analysis

Abstract: The paper presents an intelligent system to automatically infer trends in the public opinion regarding the stance towards the vaccination topic: it enables the detection of significant opinion shifts, which can be possibly explained with the occurrence of specific social context-related events. The Italian setting has been taken as the reference use case. The source of information exploited by the system is represented by the collection of vaccine-related tweets, fetched from Twitter according to specific criteria; subsequently, tweets undergo a textual elaboration and a final classification to detect the expressed stance towards vaccination (i.e. in favor, not in favor, and neutral). In tuning the system, we tested multiple combinations of different text representations and classification approaches: the best accuracy was achieved by the scheme that adopts the bag-of-words, with stemmed n-grams as tokens, for text representation and the support vector machine model for the classification. By presenting the results of a monitoring campaign lasting 10 months, we show that the system may be used to track and monitor the public opinion about vaccination decision making, in a low-cost, real-time, and quick fashion. Finally, we also verified that the proposed scheme for continuous tweet classification does not seem to suffer particularly from concept drift, considering the time span of the monitoring campaign.

Web-Based Interventions for Weight Loss or Weight Loss Maintenance in Overweight and Obese People: A Systematic Review of Systematic Reviews

Abstract: Background: Weight loss is challenging and maintenance of weight loss is problematic. Web-based programs offer good potential for delivery of interventions for weight loss or weight loss maintenance. However, the precise impact of Web-based weight management programs is still unclear. Objective: The purpose of this meta-systematic review was to provide a comprehensive summary of the efficacy of Web-based interventions for weight loss and weight loss maintenance. Methods: Electronic databases were searched for systematic reviews and meta-analyses that included at least one study investigating the effect of a Web-based intervention on weight loss and/or weight loss maintenance among samples of overweight and/or obese individuals. Twenty identified reviews met the inclusion criteria. The Revised Assessment of Multiple SysTemAtic Reviews (R-AMSTAR) was used to assess methodological quality of reviews. All included reviews were of sufficient methodological quality (R-AMSTAR score ≥22). Key methodological and outcome data were extracted from each review. Results: Web-based interventions for both weight loss and weight loss maintenance were more effective than minimal or control conditions. However, when contrasted with comparable non-Web-based interventions, results were less consistent across reviews. Conclusions: Overall, the efficacy of weight loss maintenance interventions was stronger than the efficacy of weight loss interventions, but further evidence is needed to more clearly understand the efficacy of both types of Web-based interventions. Trial Registration: PROSPERO 2015: CRD42015029377; http://www.crd.york.ac.uk/PROSPERO/display_record.asp? ID=CRD42015029377 (Archived by WebCite at http://www.webcitation.org/6qkSafdCZ)

Cognitive behavioral therapy to aid weight loss in obese patients: current perspectives.

Abstract: Obesity is a chronic condition associated with risk factors for many medical complications and comorbidities such as cardiovascular diseases, some types of cancer, osteoarthritis, hypertension, dyslipidemia, hypercholesterolemia, type-2 diabetes, obstructive sleep apnea syndrome, and different psychosocial issues and psychopathological disorders. Obesity is a highly complex, multifactorial disease: genetic, biological, psychological, behavioral, familial, social, cultural, and environmental factors can influence in different ways. Evidence-based strategies to improve weight loss, maintain a healthy weight, and reduce related comorbidities typically integrate different interventions: dietetic, nutritional, physical, behavioral, psychological, and if necessary, pharmacological and surgical ones. Such treatments are implemented in a multidisciplinary context with a clinical team composed of endocrinologists, nutritionists, dietitians, physiotherapists, psychiatrists, psychologists, and sometimes surgeons. Cognitive behavioral therapy (CBT) is traditionally recognized as the best established treatment for binge eating disorder and the most preferred intervention for obesity, and could be considered as the first-line treatment among psychological approaches, especially in a long-term perspective; however, it does not necessarily produce a successful weight loss. Traditional CBT for weight loss and other protocols, such as enhanced CBT, enhanced focused CBT, behavioral weight loss treatment, therapeutic education, acceptance and commitment therapy, and sequential binge, are discussed in this review. The issue of long-term weight management of obesity, the real challenge in outpatient settings and in lifestyle modification, is discussed taking into account the possible contribution of mHealth and the stepped-care approach in health care.

Multi-objective evolutionary design of granular rule-based classifiers

Abstract: In the last years, rule-based systems have been widely employed in several different application domains. The performance of these systems is strongly affected by the process of information granulation, which defines in terms of specific information granules such as sets, fuzzy sets and rough sets, the labels used in the rules. Generally, information granules are either provided by an expert, when possible, or extracted from the available data. In the framework of rule-based classifiers, we investigate the importance of determining an effective information granulation from data, preserving the comprehensibility of the granules. We show how the accuracies of rule-based classifiers can be increased by learning number and parameters of the granules, which partition the involved variables. To perform this analysis, we exploit a multi-objective evolutionary approach to the classifier generation we have recently proposed. We discuss different levels of information granulation optimization employing both the learning of the number of granules per variable and the tuning of each granule during the evolutionary process. We show and discuss the results obtained on several classification benchmark datasets using fuzzy sets and intervals as types of information granules.